Future-Proofing MySQL for the Worldwide Data Revolution Robert Hodges, CEO.
What is Future-Proo!ng? Future-proo!ng = creating systems that last while parts change and improve MySQL is not losing out to other solutions for data management The real problem is using MySQL as a building block with other technologies that are changing rapidly 2
But Wait... Isn t MySQL Dead?? MySQL You were so open Born May 25, 1995 Died 2008, 2009, 2010, 2011, 2012,... 3
Oracle/MySQL 5.6 Replication (On the Way) Global transaction IDs Parallel slave threads Crash safe slaves Time delay replication Optimized row updates Informational log events 4
Galera Synchronous Clusters (New Arrival in 2012) Multi-master for InnoDB Synchronous replication avoids data loss Automatic cluster membership management Simple node provisioning model Based on innovative state-machine work from Fernando Pedone 5
Tungsten Async Replication (2011 Belle of the Ball) All 5.6 features including parallel replication for MySQL 5.0 onwards Optimized support for failover Backup/restore integration Programmable transaction!ltering Multi-master, fan-in, and star replication Replication to/from Oracle and batch loading of data warehouses Replication to MongoDB 6
Not Bad for a Dying Market NewSQL,'2%' NoSQL,'5%' My/NewSQL,' 5%' MySQL,'88%' (451Group) 1000" 900" 800" 700" 600" 500" 400" 300" 200" 100" 0" THE$TOTAL$MARKET$FOR$MYSQL,$NOSQL$$ AND$NEWSQL$$ Text 2011" 2012" 2013" 2014" 2015" 7
MySQL Is Part of Bigger Trends Cloud and Big Data investment dwarf the MySQL marketplace 8
21st Century CRM/Call Center Fat Client Accounting CRM Web Application CRM Web Application Call Session Data Legacy Oracle Online Transaction Processing Hadoop Analytics On-Premise Amazon Web Services Telephony Data Streams Telephony Data Streams 9
Revolutionary Challenges for MySQL Failures in unstable cloud environments Zero-downtime maintenance Rapidly growing data volumes, esp. in cloud Distributing data to geographical regions Integration between MySQL, NoSQL, commercial RDBMS Supplying real-time analytics Technology upgrade/replacement 10
The Cure for Mixed System Spaghetti Data s Encapsulated, fault-tolerant, horizontally scalable, globally accessible, integrated data 11
5 Design Patterns for Data s Fault-Tolerant DBMS-as-a-Service Horizontal DBMS Arrays Multi-Site Async Replication Real-Time Data s s 12
1. Fault-Tolerant DBMS-as-a-Service Encapsulate redundant database copies Sync and async clustering models Protect against local DBMS failure Rolling maintenance of replicas Master/ Slave Multi- Master 13
2. Horizontal DBMS Arrays Partition datasets based on RAM / storage speed / resident set size Multiple buckets per server Look-up methods for locating data in buckets Re-sharding / migration for high growth 14
3. Multi-Site Async Replication Robust protection against region/site failures Geographic distribution of data Primary/DR vs. multi-master Eventually consistent replication for SQL EU West Amazon East US East APAC Tokyo Rackspace DFW 15
4. Real-Time Data s Replicate from logs in real-time Enable apps to get data from one data source Heterogeneous transfer and transformation High performance, low application impact 16
5. s Single point of entry for applications Service catalog locates data within fabric Transparent connectivity, multiple protocols Security, auditing, performance management Application Application Stack Stack 17
Future-Proofed CRM/Call Center Data Fat Client Accounting CRM Web Application CRM Web Application Telephony Data Streams Telephony Data Streams Legacy Oracle OLTP Primary Call Session Data Hadoop Analytics OLTP DR Data Marts 18
Future-Proofed CRM/Call Center Data Fat Client Accounting CRM Web Application CRM Web Application Telephony Data Streams Telephony Data Streams Legacy Oracle OLTP Primary Call Session Data Hadoop Analytics OLTP DR Data Marts 19
MySQL Future-Proo!ng: Conclusion MySQL is doing great but the cloud and Big Data have created a new set of challenges Data s cure mixed system spaghetti and future-proof MySQL Continuent products implement fabric design patterns We are working on covering the entire Data, not just MySQL 20
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Multiple node groups per cluster D3 D3 Application D3 Application Stack Application Stack Stack Cross-site replication Cross-site replication Locate clusters; direct write/ read traffic Primary Clusters DR Clusters 22